GES Final Project: Baltimore City
Kristian Nelson
4/11/2018
#install.packages("tidycensus")
#install.packages("tidyverse")
#install.packages("tigris")
#install.packages("sf")
library(tidycensus)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
## ✔ tibble 1.4.2 ✔ dplyr 0.7.4
## ✔ tidyr 0.8.0 ✔ stringr 1.2.0
## ✔ readr 1.1.1 ✔ forcats 0.2.0## ── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(tigris)## To enable
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.##
## Attaching package: 'tigris'## The following object is masked from 'package:graphics':
##
## plotlibrary(sf)## Linking to GEOS 3.6.1, GDAL 2.1.3, proj.4 4.9.3library(RColorBrewer)
options(tigris_class = "sf")
options(tigris_use_cache = TRUE)
census_api_key("02fd95ffa4b152f4183ec87b9bc382caf029e468")## To install your API key for use in future sessions, run this function with `install = TRUE`.Before knitting document, install all packages!
Part 1:
This is where I will get the data for Baltimore City. In the get.acs function I specificed Baltimore City as the county so it would only retrieve the data for that area.
fd <- get_acs(geography = "tract",
variables = c("B08301_019","B08301_001","B19301_001"),
state = c("MD"), county = "Baltimore City", geometry = TRUE, output = "wide")## Getting data from the 2012-2016 5-year ACSmetros <- core_based_statistical_areas(cb = TRUE) %>%
filter(GEOID %in% c("12580")) %>%
select(metro_name = NAME)
balcit <- st_join(fd, metros, join = st_within, left = FALSE) ## although coordinates are longitude/latitude, st_within assumes that they are planarhead(balcit)## Simple feature collection with 6 features and 9 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -76.62806 ymin: 39.28534 xmax: -76.5797 ymax: 39.32836
## epsg (SRID): 4269
## proj4string: +proj=longlat +datum=NAD83 +no_defs
## GEOID NAME B08301_019E
## 1 24510020200 Census Tract 202, Baltimore city, Maryland 256
## 2 24510030100 Census Tract 301, Baltimore city, Maryland 193
## 3 24510040200 Census Tract 402, Baltimore city, Maryland 160
## 4 24510080301 Census Tract 803.01, Baltimore city, Maryland 41
## 5 24510090600 Census Tract 906, Baltimore city, Maryland 0
## 6 24510100200 Census Tract 1002, Baltimore city, Maryland 188
## B08301_019M B08301_001E B08301_001M B19301_001E B19301_001M
## 1 84 1184 148 39265 4172
## 2 143 1212 203 15793 2879
## 3 43 377 91 20935 4835
## 4 46 547 104 19373 4676
## 5 12 1170 357 18685 4450
## 6 135 744 222 12318 2485
## metro_name geometry
## 1 Baltimore-Columbia-Towson, MD MULTIPOLYGON (((-76.59394 3...
## 2 Baltimore-Columbia-Towson, MD MULTIPOLYGON (((-76.59968 3...
## 3 Baltimore-Columbia-Towson, MD MULTIPOLYGON (((-76.62806 3...
## 4 Baltimore-Columbia-Towson, MD MULTIPOLYGON (((-76.58436 3...
## 5 Baltimore-Columbia-Towson, MD MULTIPOLYGON (((-76.59515 3...
## 6 Baltimore-Columbia-Towson, MD MULTIPOLYGON (((-76.60727 3...Since the tenure only gives us total number of households, total number of owners, and total number of renters, I divided the amount renters by the total amount of households to get the percentage.
balcit$walking_rate <- balcit$B08301_019E / balcit$B08301_001Ebalcit.lm <- lm(balcit$B19301_001E ~ balcit$walking_rate)Part 2:
balcit_comp <- balcit %>% filter(walking_rate >= 0 & B19301_001E >= 0)#install.packages("spdep")
library(spdep)## Loading required package: sp## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loading required package: spDatalibrary(sf)
c <- poly2nb(as(balcit_comp, "Spatial"), row.names=balcit_comp$GEOID)
ww <- nb2listw(c)
balcit_comp.moran <- moran.test(balcit_comp$B19301_001E, listw=ww, randomisation=FALSE)
balcit_comp.moran##
## Moran I test under normality
##
## data: balcit_comp$B19301_001E
## weights: ww
##
## Moran I statistic standard deviate = 14.575, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.597932378 -0.005076142 0.001711726balcit_comp.z <- (balcit_comp.moran$estimate[1] - balcit_comp.moran$estimate[2]) /
balcit_comp.moran$estimate[3]
summary(balcit_comp.z)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 352.3 352.3 352.3 352.3 352.3 352.3From this code I learned that household median income and the percentage of renters are spatially autocorrelated. Using Moran’s I it was found that there was some levels of clustering between the two sets of data. The Moran’s I statistic was 0.533 which is closer to 1 than it is to 0 which indicates clustering. The p value < 2.2e-16 which is less than 0.05 which shows this statistic is significant. The z-score was then calculated to find to what extent the clustering was. The z-score came out to be 311 which is a positive score that means there is a significant amount of clustering. All of these values show that these datasets are spatially autocorrelated.
Part 3:
balcit.lm <- lm(balcit$B19301_001E ~ balcit$walking_rate)
ggplot(balcit, aes(x = balcit$walking_rate , y = balcit$B19301_001E)) +
geom_point(shape = 1) +
geom_smooth(method = "lm", se = FALSE, color = "purple")## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
The graph shown above shows that theses two datasets are in fact correlated. It shows that as household income decreases, the rental percentage increases. This means that in the Baltimore City area, families with a lower income are forced to rent the places they live at instead of purchasing a house. Families with higher income do not need to rent.
Part 4:
plot(balcit$B19301_001E, main="Per Capita Income in Census Tracts of Baltimore City",
xlab="Census Tract", ylab="Per Capita Income ($)")
plot(balcit$walking_rate, main="Walking Rate in Census Tracts of Baltimore City",
xlab="Census Tract", ylab="Walking Rate (%)")
Part 5:
library(broom)
balcit_comp.sp <- as(balcit_comp, 'Spatial')
balcit_df <- tidy(balcit_comp.sp)## Regions defined for each Polygonshead(balcit_df)## long lat order hole piece group id
## 1 -76.59394 39.29154 1 FALSE 1 1.1 1
## 2 -76.59047 39.29168 2 FALSE 1 1.1 1
## 3 -76.59034 39.28939 3 FALSE 1 1.1 1
## 4 -76.59011 39.28573 4 FALSE 1 1.1 1
## 5 -76.59356 39.28558 5 FALSE 1 1.1 1
## 6 -76.59364 39.28661 6 FALSE 1 1.1 1balcit_comp.sp$polyID <- sapply(slot(balcit_comp.sp, "polygons"), function(x) slot(x, "ID"))
balcit_df <- merge(balcit_df, balcit_comp.sp, by.x = "id", by.y="polyID")
head(balcit_df)## id long lat order hole piece group GEOID
## 1 1 -76.59394 39.29154 1 FALSE 1 1.1 24510020200
## 2 1 -76.59047 39.29168 2 FALSE 1 1.1 24510020200
## 3 1 -76.59034 39.28939 3 FALSE 1 1.1 24510020200
## 4 1 -76.59011 39.28573 4 FALSE 1 1.1 24510020200
## 5 1 -76.59356 39.28558 5 FALSE 1 1.1 24510020200
## 6 1 -76.59364 39.28661 6 FALSE 1 1.1 24510020200
## NAME B08301_019E B08301_019M
## 1 Census Tract 202, Baltimore city, Maryland 256 84
## 2 Census Tract 202, Baltimore city, Maryland 256 84
## 3 Census Tract 202, Baltimore city, Maryland 256 84
## 4 Census Tract 202, Baltimore city, Maryland 256 84
## 5 Census Tract 202, Baltimore city, Maryland 256 84
## 6 Census Tract 202, Baltimore city, Maryland 256 84
## B08301_001E B08301_001M B19301_001E B19301_001M
## 1 1184 148 39265 4172
## 2 1184 148 39265 4172
## 3 1184 148 39265 4172
## 4 1184 148 39265 4172
## 5 1184 148 39265 4172
## 6 1184 148 39265 4172
## metro_name walking_rate
## 1 Baltimore-Columbia-Towson, MD 0.2162162
## 2 Baltimore-Columbia-Towson, MD 0.2162162
## 3 Baltimore-Columbia-Towson, MD 0.2162162
## 4 Baltimore-Columbia-Towson, MD 0.2162162
## 5 Baltimore-Columbia-Towson, MD 0.2162162
## 6 Baltimore-Columbia-Towson, MD 0.2162162Below is the map for Rental Rate in Baltimore City.
library(ggplot2)
ggplot() +
geom_polygon(
data = balcit_df,
aes(x = long, y = lat, group = group,
fill = cut_number(walking_rate, 5))) +
scale_fill_brewer("Walking Rate", palette = "Blues") +
ggtitle("Walking Rate in Baltimore City") +
theme(line = element_blank(),
axis.text=element_blank(),
axis.title=element_blank(),
panel.background = element_blank()) +
coord_equal() 
Below is the map for Median Household Income in Baltimore City.
library(ggplot2)
ggplot() +
geom_polygon(
data = balcit_df,
aes(x = long, y = lat, group = group,
fill = cut_number(B19301_001M, 5))) +
scale_fill_brewer("Per Capita Income", palette = "Blues") +
ggtitle("Per Capita in Baltimore City") +
theme(line = element_blank(),
axis.text=element_blank(),
axis.title=element_blank(),
panel.background = element_blank()) +
coord_equal() 
Part 6
#install.packages("ggmap")
library(ggmap)
myLocation <- myLocation <- c(lon = -76.61, lat = 39.29)
geocode("Maryland")## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Maryland&sensor=false## Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Maryland"## lon lat
## 1 NA NABelow is a map of Median Household Income in Baltimore City using ggmap.
BaltimoreMap <- get_map(location=myLocation, zoom = 11,
source="google", maptype="terrain", crop=FALSE)## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=39.29,-76.61&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=falseggmap(BaltimoreMap) +
geom_polygon(
data = balcit_df,
aes(x = long, y = lat, group = group,
fill = cut_number(B19301_001M, 5))) +
scale_fill_brewer("Per Capita Income", palette = "RdBu") +
ggtitle("Per Capita Income in Baltimore City") +
theme(line = element_blank(),
axis.text=element_blank(),
axis.title=element_blank(),
panel.background = element_blank()) +
coord_equal() 
Below is a map of Rental Rate in Baltimore City using ggmap.
BaltimoreMap <- get_map(location=myLocation, zoom = 11,
source="google", maptype="terrain", crop=FALSE)## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=39.29,-76.61&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=falsebalcit_comp.sp <- as(balcit_comp, 'Spatial')
ggmap(BaltimoreMap) +
geom_polygon(
data = balcit_df,
aes(x = long, y = lat, group = group,
fill = cut_number(walking_rate, 5))) +
scale_fill_brewer("Walking Rate", palette = "Purples") +
ggtitle("Walking Rate in Baltimore City") +
theme(line = element_blank(),
axis.text=element_blank(),
axis.title=element_blank(),
panel.background = element_blank()) +
coord_equal() 
EXTRA CREDIT LETS GO!!
#install.packages("leaflet")
#install.packages("tmap")
library(leaflet)## Warning: package 'leaflet' was built under R version 3.4.4library(tmap)## Warning: package 'tmap' was built under R version 3.4.4pal_fun <- colorQuantile("Purples", NULL, n = 5)
p_popup <- paste0("<strong>Walking Rate: </strong>", balcit_comp.sp$walking_rate)
leaflet(balcit_comp.sp) %>%
addPolygons(
stroke = FALSE,
fillColor = ~pal_fun(walking_rate),
fillOpacity = 0.8, smoothFactor = 0.5,
popup = p_popup,
group = "Baltimore City") %>%
addTiles(group = "OSM") %>%
addProviderTiles("CartoDB.DarkMatter", group = "Carto") %>%
addLegend("bottomright",
pal=pal_fun,
values=~walking_rate,
title = 'Walking Rate in Baltimore City') %>%
addLayersControl(baseGroups = c("OSM", "Carto"),
overlayGroups = c("Baltimore City")) library(leaflet)
library(tmap)
library(classInt)
pal <- brewer.pal(5, "OrRd")
breaks_qt <- classIntervals(balcit_comp.sp$B19301_001E,
n = 5,
style = "quantile")
pal_fun <- colorQuantile("Reds", NULL, n = 5)
p_popup <- paste0("<strong>B19301_001E: </strong>", balcit_comp.sp$B19301_001E)
leaflet(balcit_comp.sp) %>%
addPolygons(
stroke = FALSE,
fillColor = ~pal_fun(B19301_001E),
fillOpacity = 0.8, smoothFactor = 0.5,
popup = p_popup,
group = "Baltimore City") %>%
addTiles(group = "OSM") %>%
addProviderTiles("CartoDB.DarkMatter", group = "Carto") %>%
addLegend("bottomright",
colors = brewer.pal(5, "YlOrRd"),
labels = paste0("$", as.character(round(breaks_qt$brks[-1]))),
title = 'Per Capita Income ') %>%
addLayersControl(baseGroups = c("OSM", "Carto"),
overlayGroups = c("Baltimore City"))